Conciseness

The body is mostly efficient, load-bearing spec (eval case template, verification methods, execution steps), but includes more than minor padding: an inspirational blog quote ("The blog says: 'If a failure taught you something important...'"), a ~55-line Skill Validation section, and Integration/Scheduled Use/Persistence sections that could be trimmed or split out.

Actionability

Concrete, executable guidance throughout: exact eval case template, concrete check blocks (`command: npm run typecheck` / `expect_exit: 0`), named tool usage ("Use Grep tool", "Run the command via Bash"), and an exact validation command (`python .claude/skills/skill-creator/scripts/quick_validate.py <target>`). Minor gaps: "Run by Pattern-Key" and "Run by Area" name the filters but give no concrete mechanics.

Workflow Clarity

The execution workflow has a clear numbered sequence with validation checkpoints (precondition check → skip, evidence-strength check → `insufficient_evidence`, "Re-run this eval to verify" recovery loop), so the batch-operation cap does not apply. It falls short of anchor 5 because the run-filtering subsections are thin one-liners and there is no consolidated run checklist.

Progressive Disclosure

No bundle files exist and the ~290-line body inlines substantial specialized content (the entire Skill Validation section and the Custom Verification Methods/mcp-scripts example) that clearly belongs in separate reference files. Section headers are clear, matching anchor 3 ("content that should be separate is inline") rather than 4.

Specificity

"Creates permanent eval cases from promoted learnings and runs regression checks against them. Turns failures into test cases that prevent silent regression" names several concrete actions in third person, but coverage has minor gaps (the create/run/list modes and index updating are not described). Matches anchor 4 rather than 5 (not comprehensive) or 3 (more than 1-2 actions).

Completeness

It explicitly answers both what ("Creates permanent eval cases from promoted learnings and runs regression checks against them") and when with concrete trigger phrases ("Use when a learning is promoted and has a clear pass/fail condition, or on cadence to verify promoted rules still hold"). This matches anchor 5; the 'when' is explicit and specific, not merely adequate as in anchor 4.

Trigger Term Quality

Natural terms are present and relevant — "eval cases", "regression checks", "test cases", "pass/fail condition", "promoted rules" — with the workflow's own trigger phrasing ("when a learning is promoted... or on cadence"). A few natural variations are missing (e.g. "regression test", "verify the harness"), matching anchor 4 rather than 5.

Distinctiveness Conflict Risk

The niche is tightly scoped to the outer loop's regress-test step ("This is the outer loop's regress-test step", "promoted learnings"), with triggers unlikely to collide with general testing or document skills. Clear niche with minimal conflict risk, matching anchor 5.